Machine Learning-Based Retrieval of Total Ozone Column Amount and Cloud Optical Depth from Irradiance Measurements-Reference-Cited by-同舟云学术

Machine Learning-Based Retrieval of Total Ozone Column Amount and Cloud Optical Depth from Irradiance Measurements

Published:2024-09-11 Issue:9 Volume:15 Page:1103
ISSN:2073-4433
Container-title:Atmosphere
language:en
Short-container-title:Atmosphere

Author:

Sztipanov Milos¹²,Krizsán Levente²,Li Wei²,Stamnes Jakob J.³,Svendby Tove⁴^ORCID,Stamnes Knut²

Affiliation:

1. Lynker at National Oceanic and Atmospheric Administration, 5830 University Research Ct, College Park, MD 20740, USA

2. Stevens Institute of Technology, Department of Physics, 1 Castle Point Terrace, Hoboken, NJ 07030, USA

3. Department of Physics and Technology, University of Bergen, 5007 Bergen, Norway

4. NILU, Instituttveien 18, 2007 Kjeller, Norway

Abstract

A machine learning algorithm combined with measurements obtained by a NILU-UV irradiance meter enables the determination of total ozone column (TOC) amount and cloud optical depth (COD). In the New York City area, a NILU-UV instrument on the rooftop of a Stevens Institute of Technology building (40.74° N, −74.03° E) has been used to collect data for several years. Inspired by a previous study [Opt. Express 22, 19595 (2014)], this research presents an updated neural-network-based method for TOC and COD retrievals. This method provides reliable results under heavy cloud conditions, and a convenient algorithm for the simultaneous retrieval of TOC and COD values. The TOC values are presented for 2014–2023, and both were compared with results obtained using the look-up table (LUT) method and measurements by the Ozone Monitoring Instrument (OMI), deployed on NASA’s AURA satellite. COD results are also provided.

Publisher

MDPI AG

Link

https://www.mdpi.com/2073-4433/15/9/1103/pdf

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